Experimental study of active control using neural networks

Significant progress has been achieved in the active control of civil engineering structures in recent years. Although many control algorithms has been proposed, only few experiments in active structural control have been performed. In this paper, active structural control experiments were carried out using a scaled model structure simulating a three-story steel frame building. The model was subjected to a base motion on a shake table. A neural network based controller was implemented to control the response of the structure. This trained neural controller was implemented to control the response of the structure. It is experimentally verified that the neural network is able to generalized to new inputs, i.e. a properly trained neural network is capable of providing sensible outputs when presented with input data that has never been used during training. Results from this experimental study indicate great promise for the control of civil engineering structures under dynamic loadings using the artificial neural network controller.